{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "6fb2d06a-3df1-410e-a6df-cd39b43dae31",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ensure_num(x):\n",
    "    return int(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a8728f-b7ef-4e46-9000-bce4188a0151",
   "metadata": {},
   "source": [
    "metrics : https://gitlab.cncf.ci/prometheus/node_exporter/blob/master/collector/perf_linux.go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "39a0cc46-2fc7-4f8c-ab77-b9eaef493fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grafana_data_perf_total(metric='node_perf_branch_instructions_total',start=1623375495,end=1623375795,step=10):\n",
    "    \n",
    "    headers = {\"Authorization\" : \"Bearer eyJrIjoiRFQ5QzU0TjVxbFJpM2g1MG1UeXdjbXdRcHNsRHhYaTkiLCJuIjoidGsiLCJpZCI6MX0=\"}\n",
    "    url = 'http://172.16.101.5:30965/api/datasources/proxy/2/api/v1/query_range?query={metric}&start={start}&end={end}&step={step}'\n",
    "    url=url.format(metric=metric,start=start,end=end,step=step)\n",
    "    r = requests.get(url,headers=headers)\n",
    "\n",
    "    df = r.json()['data']['result']\n",
    "    df = pd.json_normalize(df)\n",
    "    df_data = df[['values','metric.instance']]\n",
    "    dk = pd.DataFrame(columns=['time','value','metrics','node'])\n",
    "    for i,r in df_data.iterrows():\n",
    "        temp = r['values']\n",
    "        metric = 'branch_instructions_15'\n",
    "        l = int(temp[0][1])\n",
    "        for i in temp:\n",
    "            d = int (i[1])-l\n",
    "            if d !=0:\n",
    "                df1 = pd.DataFrame([[i[0],int(d),metric,r['metric.instance']]],columns=['time','value','metrics','node'])\n",
    "                dk = dk.append(df1)\n",
    "            l = int(i[1])\n",
    "#     dk['time'] = pd.to_datetime(dk['time'], unit='s')\n",
    "    dk['value'] = dk.apply(lambda x: ensure_num(x.value), axis=1)\n",
    "    return dk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "5ade37f8-9523-42e0-8eaa-e68229796508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>metrics</th>\n",
       "      <th>node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375505</td>\n",
       "      <td>110302</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.197:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375515</td>\n",
       "      <td>171463</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.197:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375525</td>\n",
       "      <td>70988</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.197:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375535</td>\n",
       "      <td>121419</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.197:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375545</td>\n",
       "      <td>216888</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.197:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375755</td>\n",
       "      <td>86759</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.222:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375765</td>\n",
       "      <td>120601</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.222:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375775</td>\n",
       "      <td>257447</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.222:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375785</td>\n",
       "      <td>96986</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.222:9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623375795</td>\n",
       "      <td>143374</td>\n",
       "      <td>branch_instructions_15</td>\n",
       "      <td>172.169.8.222:9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4650 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time   value                 metrics                node\n",
       "0   1623375505  110302  branch_instructions_15  172.169.8.197:9100\n",
       "0   1623375515  171463  branch_instructions_15  172.169.8.197:9100\n",
       "0   1623375525   70988  branch_instructions_15  172.169.8.197:9100\n",
       "0   1623375535  121419  branch_instructions_15  172.169.8.197:9100\n",
       "0   1623375545  216888  branch_instructions_15  172.169.8.197:9100\n",
       "..         ...     ...                     ...                 ...\n",
       "0   1623375755   86759  branch_instructions_15  172.169.8.222:9100\n",
       "0   1623375765  120601  branch_instructions_15  172.169.8.222:9100\n",
       "0   1623375775  257447  branch_instructions_15  172.169.8.222:9100\n",
       "0   1623375785   96986  branch_instructions_15  172.169.8.222:9100\n",
       "0   1623375795  143374  branch_instructions_15  172.169.8.222:9100\n",
       "\n",
       "[4650 rows x 4 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dk = get_grafana_data_perf_total(metric='node_perf_cache_misses_total')\n",
    "dk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "d14ed374-8946-4590-be70-57bb5851020c",
   "metadata": {},
   "outputs": [],
   "source": [
    "node_perf_cache_tlb_instr_read_misses_total = get_grafana_data(metric='node_perf_cache_tlb_instr_read_misses_total')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47e1a9a6-de4f-49a1-9e58-14f0b24a5b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Load an example dataset with long-form data\n",
    "fmri = node_perf_cache_tlb_instr_read_misses_total\n",
    "\n",
    "# Plot the responses for different events and regions\n",
    "sns.relplot(x='time',y=\"value\",\n",
    "             hue=\"node\", kind='line', palette='gist_stern',\n",
    "             data=fmri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a59fd8c2-becf-4ea5-a2b7-5a66db33614b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e65e302f-e49d-4a21-a4cc-446a686102aa",
   "metadata": {},
   "source": [
    "node_perf_cache_misses_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "de2d2e63-9065-4f06-acd5-7cbb6acc1f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fba92663df0>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 511.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = get_grafana_data(metric='node_perf_cache_misses_total')\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "\n",
    "fmri = data\n",
    "\n",
    "sns.relplot(x='time',y=\"value\",\n",
    "             hue=\"node\", kind='line',\n",
    "             data=fmri)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b00e33a-fdbb-49b1-b888-1f54b009ca29",
   "metadata": {},
   "source": [
    "# 获取容器内cpu、mem等\n",
    "1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4e39b3f-52e3-4855-aefa-179d9af487dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from prometheus_api_client import PrometheusConnect\n",
    "# prom = PrometheusConnect(url =\"http://172.16.101.5:30090\", disable_ssl=True)\n",
    "# # prom.all_metrics()\n",
    "\n",
    "# p = {'start':1623638509.832, 'end':1623642109.832,'step':14,'_':1623316231081}\n",
    "# query= 'sum (container_memory_working_set_bytes{origin_prometheus=~\"\",container =~\"alarmprovider|apigateway|controller|couchdb|grafana|invoker|kafka|kafkaprovider|nginx|prometheus|redis|user-action|user-events|zookeeper\",container !=\"\",container!=\"POD\",namespace=~\"ow\"}) by (container)'\n",
    "# json = prom.custom_query(query=query,params=p)\n",
    "# json\n",
    "# # df = pd.json_normalize(json)\n",
    "# # df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "128255fd-0dff-4644-bf08-3b138f6aac1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_timeseries_data_from_grafana(metric='sum(kube_pod_container_resource_limits_memory_bytes{namespace=~\"ow\"}) by (container,pod)',\n",
    "                                     start=1623656475,end=1623657375,step=15):\n",
    "    headers = {\"Authorization\" : \"Bearer eyJrIjoiRFQ5QzU0TjVxbFJpM2g1MG1UeXdjbXdRcHNsRHhYaTkiLCJuIjoidGsiLCJpZCI6MX0=\"}\n",
    "    url = 'http://172.16.101.5:30965/api/datasources/proxy/2/api/v1/query_range?query={metric}&start={start}&end={end}&step={step}'\n",
    "    url=url.format(metric=metric,start=start,end=end,step=step)\n",
    "    r = requests.get(url,headers=headers)\n",
    "    df = pd.json_normalize(r.json()['data']['result'])\n",
    "\n",
    "    dk = pd.DataFrame(columns=['time','value','metrics','node'])\n",
    "\n",
    "    for i,r in df.iterrows():\n",
    "        ds = pd.DataFrame(r['values'],columns=['time','value'])\n",
    "        ds['metrics'] = 'mem_limit_pod'\n",
    "        ds['node']  = r['metric.pod']\n",
    "        dk = dk.append(ds)\n",
    "    return dk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "7e370d44-23a7-4884-b395-10b8a0f9529a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>metrics</th>\n",
       "      <th>node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623656475</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-4-prewarm-nodejs10</td>\n",
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       "      <th>1</th>\n",
       "      <td>1623656490</td>\n",
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       "      <td>wskowdev-invoker-00-4-prewarm-nodejs10</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>1623656520</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-4-prewarm-nodejs10</td>\n",
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       "      <th>4</th>\n",
       "      <td>1623656535</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-4-prewarm-nodejs10</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>56</th>\n",
       "      <td>1623657315</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1623657330</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
       "    </tr>\n",
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       "      <th>58</th>\n",
       "      <td>1623657345</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
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       "      <th>59</th>\n",
       "      <td>1623657360</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
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       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1623657375</td>\n",
       "      <td>268435456</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time      value        metrics  \\\n",
       "0   1623656475  268435456  mem_limit_pod   \n",
       "1   1623656490  268435456  mem_limit_pod   \n",
       "2   1623656505  268435456  mem_limit_pod   \n",
       "3   1623656520  268435456  mem_limit_pod   \n",
       "4   1623656535  268435456  mem_limit_pod   \n",
       "..         ...        ...            ...   \n",
       "56  1623657315  268435456  mem_limit_pod   \n",
       "57  1623657330  268435456  mem_limit_pod   \n",
       "58  1623657345  268435456  mem_limit_pod   \n",
       "59  1623657360  268435456  mem_limit_pod   \n",
       "60  1623657375  268435456  mem_limit_pod   \n",
       "\n",
       "                                       node  \n",
       "0    wskowdev-invoker-00-4-prewarm-nodejs10  \n",
       "1    wskowdev-invoker-00-4-prewarm-nodejs10  \n",
       "2    wskowdev-invoker-00-4-prewarm-nodejs10  \n",
       "3    wskowdev-invoker-00-4-prewarm-nodejs10  \n",
       "4    wskowdev-invoker-00-4-prewarm-nodejs10  \n",
       "..                                      ...  \n",
       "56  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "57  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "58  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "59  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "60  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "\n",
       "[122 rows x 4 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6137210-0101-4e52-98c4-ce18ed983d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 'sum(container_memory_working_set_bytes{namespace=~\"ow\"}) by (container,pod)'\n",
    "\n",
    "dk = get_timeseries_data_from_grafana(metric=m)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "1e094a35-2b6a-431a-a84b-f1859d0c709a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>56</th>\n",
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       "      <th>57</th>\n",
       "      <td>1623657330</td>\n",
       "      <td>17235968</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
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       "      <th>58</th>\n",
       "      <td>1623657345</td>\n",
       "      <td>17235968</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
       "    </tr>\n",
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       "      <th>59</th>\n",
       "      <td>1623657360</td>\n",
       "      <td>17235968</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
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       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1623657375</td>\n",
       "      <td>17235968</td>\n",
       "      <td>mem_limit_pod</td>\n",
       "      <td>wskowdev-invoker-00-40-prewarm-nodejs10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>549 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time     value        metrics  \\\n",
       "0   1623656475   1069056  mem_limit_pod   \n",
       "1   1623656490   1069056  mem_limit_pod   \n",
       "2   1623656505   1069056  mem_limit_pod   \n",
       "3   1623656520   1069056  mem_limit_pod   \n",
       "4   1623656535   1069056  mem_limit_pod   \n",
       "..         ...       ...            ...   \n",
       "56  1623657315  17235968  mem_limit_pod   \n",
       "57  1623657330  17235968  mem_limit_pod   \n",
       "58  1623657345  17235968  mem_limit_pod   \n",
       "59  1623657360  17235968  mem_limit_pod   \n",
       "60  1623657375  17235968  mem_limit_pod   \n",
       "\n",
       "                                       node  \n",
       "0                           owdev-invoker-0  \n",
       "1                           owdev-invoker-0  \n",
       "2                           owdev-invoker-0  \n",
       "3                           owdev-invoker-0  \n",
       "4                           owdev-invoker-0  \n",
       "..                                      ...  \n",
       "56  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "57  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "58  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "59  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "60  wskowdev-invoker-00-40-prewarm-nodejs10  \n",
       "\n",
       "[549 rows x 4 columns]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dplot= dk.loc[dk.node.str.contains(\"invoker\") ]\n",
    "dplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96bf7f89-9aea-4b3a-9f18-6c28b5beb3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "\n",
    "fmri = dplot\n",
    "\n",
    "sns.relplot(x='time',y=\"value\",\n",
    "             hue=\"node\", \n",
    "             data=fmri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9095a083-04f3-4109-b0b2-0f1cfdc97f08",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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